Special lssues

Artificial Intelligence for Smart Cities

Submission Deadline: 20 October 2022 (closed)

Guest Editors

Dr. Tuong Le, Ton Duc Thang University, Viet Nam.
Assoc. Prof. Bay Vo, Ho Chi Minh City University of Technology (HUTECH), Viet Nam.
Prof. Tzung-Pei Hong, National University of Kaohsiung, Taiwan.


Smart cities are an inevitable trend as information technology infrastructure is increasingly developed. In such cities, Internet of Things (IoT) devices such as sensors, cameras and actuators work to make everyday tasks easier and more efficient, while relieving pain points related to public safety, traffic, and environmental issues. Fortunately, intelligent devices are becoming increasingly popular, therefore, huge amounts of data have been collected every day Analysis of this data using Artificial Intelligence and Machine Learning techniques will be very helpful in improving the overall performance of the smart cities’ services. They can change the way smart cities operate in various fields such as healthcare, energy, transportation, surveillance and many others. 
This special issue motivates and inspires researchers in both academic and industry to present their works utilizing Artificial Intelligence and machine learning in the development of a smart city.

Topics: Potential topics include but are not limited to the following:

• AI for smart mobility and transportation

• AI for smart video surveillance
• AI for smart healthcare 
• AI for smart emergency management
• AI for smart traffic system operations
• AI for smart energy systems 
• AI for smart buildings and smart home




Artificial Intelligence
Smart Cities
Internet of Things

Published Papers

  • Open Access


    Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

    Song Yu, Aiping Luo, Xiang Wang
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1877-1893, 2023, DOI:10.32604/iasc.2023.039132
    (This article belongs to this Special Issue: Artificial Intelligence for Smart Cities)
    Abstract Railway passenger flow forecasting can help to develop sensible railway schedules, make full use of railway resources, and meet the travel demand of passengers. The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow. Most of the previous studies used only a single feature for prediction and lacked correlations, resulting in suboptimal performance. To address the above-mentioned problem, we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network (F-SAGCN). First, we constructed the passenger flow relations graph (RG) based on the… More >

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